scholarly journals Analysis of the landslide susceptibility map using frequency ratio method in sub-sub-Watershed Mamasa

2021 ◽  
Vol 886 (1) ◽  
pp. 012088
Author(s):  
Rizki Amaliah ◽  
Andang Suryana Soma ◽  
Baharruddin Mappangaja ◽  
Friska Mambela
Keyword(s):  
Success Rate ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Causative Factor ◽  
Hydropower Plant ◽  
Ratio Method ◽  
Susceptibility Map ◽  
Contributing Factors ◽  
Landslide Susceptibility Map ◽  
Frequency Ratio Method

Abstract Landslides that often occur in the Subs watershed of Mamasa increase the sedimentation rate so that the Bakaru hydropower plant becomes less than optimal. The contributing factors to lanslide susceptibility are land closure, lithology, curve, slope direction aspect, slope, precipitation, fault distance, and river distance. The research aims to determine the most influential erosion causative factor in Mamasa Sub-watershed by building a landslide susceptibility map using the frequency ratio method. The most significant factor is land closure, with a value of 2.03, indicating a high probability of lanslide events. The model’s success rate and prediction rate’s success rate were expressed fairly well with 0.754 and 0.752. Based on the insanity map, the Region is very high and high at 23.74% and 12.52%; insanity is moderate, low, and very low consecutively at 27.44 %, 23.77, and 12.33%.

Application of Airborne LiDAR-Derived Parameters and Probabilistic-Based Frequency Ratio Model in Landslide Susceptibility Mapping

2012 ◽  
Vol 225 ◽  
pp. 442-447 ◽  
Author(s):  
Biswajeet Pradhan ◽  
Zulkiflee Abd. Latif ◽  
Siti Nur Afiqah Aman
Keyword(s):  
High Resolution ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Susceptibility Mapping ◽  
Airborne Lidar ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Ratio Model ◽  
Landslide Susceptibility Map ◽  
Frequency Ratio Model

The escalating number of occurrences of natural hazards such as landslides has raised a great interest among the geoscientists. Due to the extremely high number of point’s returns, airborne LiDAR permits the formation of more accurate DEM compared to other space borne and airborne remote sensing techniques. This study aims to assess the capability of LiDAR derived parameters in landslide susceptibility mapping. Due to frequent occurrence of landslides, Ulu Klang in Selangor state in Malaysia has been considered as application site. A high resolution of airborne LiDAR DEM was constructed to produce topographic attributes such as slope, curvature and aspect. These data were utilized to derive secondary deliverables of landslide parameters such as topographic wetness index (TWI), surface area ratio (SAR) and stream power index (SPI). A probabilistic based frequency ratio model was applied to establish the spatial relationship between the landslide locations and each landslide related factors. Subsequently, factor ratings were summed up to yield Landslide Susceptibility Index (LSI) and finally a landslide susceptibility map was prepared. To test the model performance, receiver operating characteristics (ROC) curve was carried out together with area under curve (AUC) analysis. The produced landslide susceptibility map demonstrated that high resolution airborne LiDAR data has huge potential in landslide susceptibility mapping.

scholarly journals Landslide susceptibility map using certainty factor for hazard mitigation in mountainous areas of Ujung-loe watershed in South Sulawesi

2018 ◽  
Vol 2 (1) ◽  
pp. 79 ◽  
Author(s):  
Andang Suryana Soma ◽  
Tetsuya Kubota
Keyword(s):  
Landslide Susceptibility ◽  
Causative Factor ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Landslide Occurrence ◽  
Sparse Vegetation ◽  
Dense Vegetation ◽  
South Sulawesi ◽  
Landslide Hazards ◽  
Causative Factors

This study aims to build a landslide susceptibility map (LSM) by using certainty factor (CF) models for mitigation of landslide hazards and mitigation for people who live near to the forest. In the study area, the mountainous area of the Ujung-loe watersheds of South Sulawesi, Indonesia, information on landslides were derived from aerial photography using time series data images from Google Earth Pro© from 2012 to 2016 and field surveys. The LSM was built by using a CF model with eleven causative factors. The results indicated that the causative factor with the highest impact on the probability of landslide occurrence is the class of change from dense vegetation to sparse vegetation (4-1), with CF value 0.95. The CF method proved to be an excellent method for producing a landslide susceptibility map for mitigation with an area under curve (AUC) success rate of 0.831, and AUC predictive rate 0.830 and 85.28% of landslides validation fell into the high to very high class. In conclusion, correlations between landslide occurrence with causative factors shows an overall highest LUC causative factor related to the class of change from dense vegetation to sparse vegetation, resulting in the highest probability of landslide occurrence. Thus, forest areas uses at these locations should prioritize maintaining dense vegetation and involving the community in protection measures to reduce the occurrence of landslide risk. LSM models that apply certainty factors can serve as guidelines for mitigation of people living in this area to pay attention to landslide hazards with high and very high landslide vulnerability and to be careful to avoid productive activities at those locations.

scholarly journals FRMOD, a Python tool for statistical landslide susceptibility assessment

2021 ◽  
Vol 3 ◽  
pp. 1-6
Author(s):  
Dávid Gerzsenyi
Keyword(s):  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Predictive Performance ◽  
Ratio Method ◽  
Analysis Method ◽  
Frequency Distributions ◽  
Landslide Occurrence ◽  
Susceptibility Analysis ◽  
Gis Tools ◽  
Frequency Ratio Method

Abstract. Locating landslide-prone slopes is important, as landslides often threaten life or property where they occur. There is an abundance of statistical methods in the literature for estimating susceptibility to landslides, i.e., the likelihood of landslide occurrence based on the analyzed conditions. Still, there is a lack of readily available GIS tools for landslide susceptibility analysis, making it hard to reproduce or compare the results of different susceptibility assessments. The FRMOD is a Python-based tool for conducting landslide susceptibility analysis with the frequency ratio method. The frequency ratio method yields susceptibility estimates by comparing the frequency distributions of a set of variables from the sample landslide areas to the distributions for the whole study area. The estimates show the level of similarity to the sample landslides. The two main inputs of the tool are the raster grids of the analyzed continuous (e.g., elevation, slope) and thematic (e.g., lithology) variables and the mask grid that marks the landslide and the non-landslide areas. The analysis is performed with cross-validation to measure the predictive performance of the model. Data computed during the analysis is stored along the final susceptibility estimates and the supplementary statistics. The script reads and writes GDAL-compatible rasters, while the statistics can be saved as text files. Basic plotting functionalities for the grids and the statistics are also built-in to quicken the evaluation of the results. FRMOD enables the swift testing of different analysis setups and to apply the same analysis method for different areas with relative ease.

scholarly journals Planet Image-Based Inventorying and Machine Learning-Based Susceptibility Mapping for the Landslides Triggered by the 2018 Mw6.6 Tomakomai, Japan Earthquake

2019 ◽  
Vol 11 (8) ◽  
pp. 978 ◽  
Author(s):  
Xiaoyi Shao ◽  
Siyuan Ma ◽  
Chong Xu ◽  
Pengfei Zhang ◽  
Boyu Wen ◽  
...  
Keyword(s):  
Success Rate ◽  
Landslide Susceptibility ◽  
Slope Failure ◽  
Slope Angle ◽  
Training Dataset ◽  
Support Vector ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Test Dataset ◽  
Prediction Rate

The 5 September 2018 (UTC time) Mw6.6 earthquake of Tomakomai, Japan has triggered about 10,000 landslides with high density, causing widespread concern. We attempted to establish a detailed inventory of this slope failure and use proper methods to assess landslide susceptibility in the entire affected area. To this end we applied the logistic regression (LR) and the support vector machine (SVM) for this study. Based on high-resolution (3 m) optical satellite images (planet image) before and after the earthquake, we delineated 9295 individual landslides triggered by the earthquake, occupying an area of 30.96 km2. Ten controlling factors were selected for susceptibility analysis, including elevation, slope angle, aspect, curvature, distances to faults, distances to the epicenter, Peak ground acceleration (PGA), distance to rivers, distances to roads and lithology. Using the LR and SVM, two landslide susceptibility maps were produced for the study area. The results show that in the LR model, the success rate is 84.7% between the landslide susceptibility map and the training dataset, and the prediction rate is 83.9% shown by comparing the test dataset and the landslide susceptibility map. In the SVM model, a success rate of 90.9% exists between the susceptibility map and the test samples, and a prediction rate of 87.1% from comparison of the test dataset and the landslides susceptibility map. In comparison, the performance of the SVM is slightly better than the LR model.

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